An Investigation Into the Efficacy of Deep Learning Tools for Big Data Analysis in Health Care

2020 ◽  
pp. 1826-1838
Author(s):  
Rojalina Priyadarshini ◽  
Rabindra K. Barik ◽  
Chhabi Panigrahi ◽  
Harishchandra Dubey ◽  
Brojo Kishore Mishra
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Large Data ◽  
Big Data Analysis ◽  
Optimization Techniques ◽  
Data Sets ◽  
Learning Tools ◽  
Healthcare Applications ◽  
Proper Training ◽  
Future Prediction

This article describes how machine learning (ML) algorithms are very useful for analysis of data and finding some meaningful information out of them, which could be used in various other applications. In the last few years, an explosive growth has been seen in the dimension and structure of data. There are several difficulties faced by conventional ML algorithms while dealing with such highly voluminous and unstructured big data. The modern ML tools are designed and used to deal with all sorts of complexities of data. Deep learning (DL) is one of the modern ML tools which are commonly used to find the hidden structure and cohesion among these large data sets by giving proper training in parallel platforms with intelligent optimization techniques to further analyze and interpret the data for future prediction and classification. This article focuses on the use of DL tools and software which are used in past couple of years in various areas and especially in the area of healthcare applications.

An Investigation Into the Efficacy of Deep Learning Tools for Big Data Analysis in Health Care

2020 ◽  
pp. 654-666
Author(s):  
Rojalina Priyadarshini ◽  
Rabindra K. Barik ◽  
Chhabi Panigrahi ◽  
Harishchandra Dubey ◽  
Brojo Kishore Mishra
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Large Data ◽  
Big Data Analysis ◽  
Optimization Techniques ◽  
Data Sets ◽  
Learning Tools ◽  
Healthcare Applications ◽  
Proper Training ◽  
Future Prediction

This article describes how machine learning (ML) algorithms are very useful for analysis of data and finding some meaningful information out of them, which could be used in various other applications. In the last few years, an explosive growth has been seen in the dimension and structure of data. There are several difficulties faced by conventional ML algorithms while dealing with such highly voluminous and unstructured big data. The modern ML tools are designed and used to deal with all sorts of complexities of data. Deep learning (DL) is one of the modern ML tools which are commonly used to find the hidden structure and cohesion among these large data sets by giving proper training in parallel platforms with intelligent optimization techniques to further analyze and interpret the data for future prediction and classification. This article focuses on the use of DL tools and software which are used in past couple of years in various areas and especially in the area of healthcare applications.

An Investigation Into the Efficacy of Deep Learning Tools for Big Data Analysis in Health Care

2018 ◽  
Vol 10 (3) ◽  
pp. 1-13 ◽  
Author(s):  
Rojalina Priyadarshini ◽  
Rabindra K. Barik ◽  
Chhabi Panigrahi ◽  
Harishchandra Dubey ◽  
Brojo Kishore Mishra
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Large Data ◽  
Optimization Techniques ◽  
Large Data Sets ◽  
Data Sets ◽  
Learning Tools ◽  
Healthcare Applications ◽  
Proper Training ◽  
Future Prediction

This article describes how machine learning (ML) algorithms are very useful for analysis of data and finding some meaningful information out of them, which could be used in various other applications. In the last few years, an explosive growth has been seen in the dimension and structure of data. There are several difficulties faced by conventional ML algorithms while dealing with such highly voluminous and unstructured big data. The modern ML tools are designed and used to deal with all sorts of complexities of data. Deep learning (DL) is one of the modern ML tools which are commonly used to find the hidden structure and cohesion among these large data sets by giving proper training in parallel platforms with intelligent optimization techniques to further analyze and interpret the data for future prediction and classification. This article focuses on the use of DL tools and software which are used in past couple of years in various areas and especially in the area of healthcare applications.

scholarly journals FinTech as an innovation challenge: from big data to sustainable development

2020 ◽  
Vol 166 ◽  
pp. 13027
Author(s):  
Anzhela Ignatyuk ◽  
Olena Liubkina ◽  
Tetiana Murovana ◽  
Alina Magomedova
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Large Data ◽  
Big Data Analysis ◽  
Current Approach ◽  
Data Sets ◽  
Human Society ◽  
Public And Private ◽  
Global Trends ◽  
Green Business

Driving force of human society development is elimination contradiction between unlimited usage of natural resources during economic activity of enterprises, environment pollution as a result of such activity and limited natural, energy and other resources. Research results on economic and environmental issues of green business management showed that there are several basic types of problems at present which arise at enterprises during collecting and processing data on the results of their activities. The authors analyzed how public sector and green business is catching up on global trend towards broader use of the big data analysis to serve public interests and increase efficiency of business activities. In order to detect current approach to big data analysis in public and private sectors authors conduct interviews with stakeholders. The paper concludes with the analysis what changes in approaches to the big data analysis in public and private sectors have to be made in order to comply with the global trends in greening the economy. Application of FinTech, methods of processing large data sets and tools for implementing the principles of greening the economy will enable to increase the investment attractiveness of green business and will simplify the interaction between the state and enterprises.

scholarly journals Deep Learning Security Systems

2019 ◽  
Vol 8 (6S3) ◽  
pp. 1823-1826
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Data Analysis ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Security Systems ◽  
Abstract Knowledge

Big Data Analytics and Deep Learning are not supposed to be two entirely different concepts. Big Data means extremely huge large data sets that can be analyzed to find patterns, trends. One technique that can be used for data analysis so that able to help us find abstract patterns in Big Data is Deep Learning. If we apply Deep Learning to Big Data, we can find unknown and useful patterns that were impossible so far. With the help of Deep Learning, AI is getting smart. There is a hypothesis in this regard, the more data, the more abstract knowledge. So a handy survey of Big Data, Deep Learning and its application in Big Data is necessary.

scholarly journals An Analysis of Data Processing for Big Data Analytics

2021 ◽  
pp. 130-138
Author(s):  
Steve Blair ◽  
Jon Cotter
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Data Analytics ◽  
High Performance ◽  
Big Data Analytics ◽  
Large Data ◽  
Data Availability ◽  
Large Data Sets ◽  
Data Sets ◽  
Wide Range

The need for high-performance Data Mining (DM) algorithms is being driven by the exponentially increasing data availability such as images, audio and video from a variety of domains, including social networks and the Internet of Things (IoT). Deep learning is an emerging field of pattern recognition and Machine Learning (ML) study right now. It offers computer simulations of numerous nonlinear processing layers of neurons that may be used to learn and interpret data at higher degrees of abstractions. Deep learning models, which may be used in cloud technology and huge computational systems, can inherently capture complex structures of large data sets. Heterogeneousness is one of the most prominent characteristics of large data sets, and Heterogeneous Computing (HC) causes issues with system integration and Advanced Analytics. This article presents HC processing techniques, Big Data Analytics (BDA), large dataset instruments, and some classic ML and DM methodologies. The use of deep learning to Data Analytics is investigated. The benefits of integrating BDA, deep learning, HPC (High Performance Computing), and HC are highlighted. Data Analytics and coping with a wide range of data are discussed.

scholarly journals A multilevel approach to big data analysis using analytic tools and actor network theory

2018 ◽  
Vol 20 (1) ◽  
Author(s):  
Tiko Iyamu
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Network Theory ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Actor Network Theory ◽  
Big Data Analysis ◽  
Data Sets ◽  
Multilevel Approach ◽  
Actor Network

Background: Over the years, big data analytics has been statically carried out in a programmed way, which does not allow for translation of data sets from a subjective perspective. This approach affects an understanding of why and how data sets manifest themselves into various forms in the way that they do. This has a negative impact on the accuracy, redundancy and usefulness of data sets, which in turn affects the value of operations and the competitive effectiveness of an organisation. Also, the current single approach lacks a detailed examination of data sets, which big data deserve in order to improve purposefulness and usefulness.Objective: The purpose of this study was to propose a multilevel approach to big data analysis. This includes examining how a sociotechnical theory, the actor network theory (ANT), can be complementarily used with analytic tools for big data analysis.Method: In the study, the qualitative methods were employed from the interpretivist approach perspective.Results: From the findings, a framework that offers big data analytics at two levels, micro- (strategic) and macro- (operational) levels, was developed. Based on the framework, a model was developed, which can be used to guide the analysis of heterogeneous data sets that exist within networks.Conclusion: The multilevel approach ensures a fully detailed analysis, which is intended to increase accuracy, reduce redundancy and put the manipulation and manifestation of data sets into perspectives for improved organisations’ competitiveness.

scholarly journals Deep learning neural network seismic big-data analysis of earthquake correlations in distinct seismic regions

2021 ◽  
Vol 8 (84) ◽  
Author(s):  
Antonios Konstantaras ◽  
Nikolaos S. Petrakis ◽  
Theofanis Frantzeskakis ◽  
Emmanouil Markoulakis ◽  
Katerina Kabassi ◽  
...  
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Deep Learning ◽  
Data Analysis ◽  
Big Data Analysis ◽  
Seismic Regions ◽  
Deep Learning Neural Network

scholarly journals Bought and Sold: Exploring the Effects of Big Data on User Agency and Commodification

2021 ◽  
Author(s):  
Kristia M. Pavlakos
Keyword(s):  
Social Sciences ◽  
Big Data ◽  
Data Privacy ◽  
Large Data ◽  
Data Sets ◽  
Privacy Regulation ◽  
Scholarly Literature ◽  
User Interests ◽  
The Social ◽  
The Relationship

Big Data1is a phenomenon that has been increasingly studied in the academy in recent years, especially in technological and scientific contexts. However, it is still a relatively new field of academic study; because it has been previously considered in mainly technological contexts, more attention needs to be drawn to the contributions made in Big Data scholarship in the social sciences by scholars like Omar Tene and Jules Polonetsky, Bart Custers, Kate Crawford, Nick Couldry, and Jose van Dijk. The purpose of this Major Research Paper is to gain insight into the issues surrounding privacy and user rights, roles, and commodification in relation to Big Data in a social sciences context. The term “Big Data” describes the collection, aggregation, and analysis of large data sets. While corporations are usually responsible for the analysis and dissemination of the data, most of this data is user generated, and there must be considerations regarding the user’s rights and roles. In this paper, I raise three main issues that shape the discussion: how users can be more active agents in data ownership, how consent measures can be made to actively reflect user interests instead of focusing on benefitting corporations, and how user agency can be preserved. Through an analysis of social sciences scholarly literature on Big Data, privacy, and user commodification, I wish to determine how these concepts are being discussed, where there have been advancements in privacy regulation and the prevention of user commodification, and where there is a need to improve these measures. In doing this, I hope to discover a way to better facilitate the relationship between data collectors and analysts, and user-generators. 1 While there is no definitive resolution as to whether or not to capitalize the term “Big Data”, in capitalizing it I chose to conform with such authors as boyd and Crawford (2012), Couldry and Turow (2014), and Dalton and Thatcher (2015), who do so in the scholarly literature.

Big Data Deep Learning Tools

2019 ◽  
pp. 220-226
Author(s):  
Nur Farhana Hordri ◽  
Siti Sophiayati Yuhaniz ◽  
Siti Mariyam Shamsuddin ◽  
Nurulhuda Firdaus Mohd Azmi
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Learning Tools
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